IPL(Indian Premier League) Data Analysis and Visualization¶

Import Libraries¶

In [1]:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px

%matplotlib inline

Load the dataset¶

In [2]:
df1 = pd.read_csv("C:\\Users\\HP\\Downloads\\archive (1)\\ipl\\IPL Ball-by-Ball 2008-2020.csv")
In [3]:
df2 = pd.read_csv("C:\\Users\\HP\\Downloads\\archive (1)\\IPL Matches 2008-2020.csv")
df2
Out[3]:
id city date player_of_match venue neutral_venue team1 team2 toss_winner toss_decision winner result result_margin eliminator method umpire1 umpire2
0 335982 Bangalore 2008-04-18 BB McCullum M Chinnaswamy Stadium 0 Royal Challengers Bangalore Kolkata Knight Riders Royal Challengers Bangalore field Kolkata Knight Riders runs 140.0 N NaN Asad Rauf RE Koertzen
1 335983 Chandigarh 2008-04-19 MEK Hussey Punjab Cricket Association Stadium, Mohali 0 Kings XI Punjab Chennai Super Kings Chennai Super Kings bat Chennai Super Kings runs 33.0 N NaN MR Benson SL Shastri
2 335984 Delhi 2008-04-19 MF Maharoof Feroz Shah Kotla 0 Delhi Daredevils Rajasthan Royals Rajasthan Royals bat Delhi Daredevils wickets 9.0 N NaN Aleem Dar GA Pratapkumar
3 335985 Mumbai 2008-04-20 MV Boucher Wankhede Stadium 0 Mumbai Indians Royal Challengers Bangalore Mumbai Indians bat Royal Challengers Bangalore wickets 5.0 N NaN SJ Davis DJ Harper
4 335986 Kolkata 2008-04-20 DJ Hussey Eden Gardens 0 Kolkata Knight Riders Deccan Chargers Deccan Chargers bat Kolkata Knight Riders wickets 5.0 N NaN BF Bowden K Hariharan
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
811 1216547 Dubai 2020-09-28 AB de Villiers Dubai International Cricket Stadium 0 Royal Challengers Bangalore Mumbai Indians Mumbai Indians field Royal Challengers Bangalore tie NaN Y NaN Nitin Menon PR Reiffel
812 1237177 Dubai 2020-11-05 JJ Bumrah Dubai International Cricket Stadium 0 Mumbai Indians Delhi Capitals Delhi Capitals field Mumbai Indians runs 57.0 N NaN CB Gaffaney Nitin Menon
813 1237178 Abu Dhabi 2020-11-06 KS Williamson Sheikh Zayed Stadium 0 Royal Challengers Bangalore Sunrisers Hyderabad Sunrisers Hyderabad field Sunrisers Hyderabad wickets 6.0 N NaN PR Reiffel S Ravi
814 1237180 Abu Dhabi 2020-11-08 MP Stoinis Sheikh Zayed Stadium 0 Delhi Capitals Sunrisers Hyderabad Delhi Capitals bat Delhi Capitals runs 17.0 N NaN PR Reiffel S Ravi
815 1237181 Dubai 2020-11-10 TA Boult Dubai International Cricket Stadium 0 Delhi Capitals Mumbai Indians Delhi Capitals bat Mumbai Indians wickets 5.0 N NaN CB Gaffaney Nitin Menon

816 rows × 17 columns

Basic information about the dataset¶

In [4]:
df2.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 816 entries, 0 to 815
Data columns (total 17 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   id               816 non-null    int64  
 1   city             803 non-null    object 
 2   date             816 non-null    object 
 3   player_of_match  812 non-null    object 
 4   venue            816 non-null    object 
 5   neutral_venue    816 non-null    int64  
 6   team1            816 non-null    object 
 7   team2            816 non-null    object 
 8   toss_winner      816 non-null    object 
 9   toss_decision    816 non-null    object 
 10  winner           812 non-null    object 
 11  result           812 non-null    object 
 12  result_margin    799 non-null    float64
 13  eliminator       812 non-null    object 
 14  method           19 non-null     object 
 15  umpire1          816 non-null    object 
 16  umpire2          816 non-null    object 
dtypes: float64(1), int64(2), object(14)
memory usage: 108.5+ KB
In [5]:
df2.head()
Out[5]:
id city date player_of_match venue neutral_venue team1 team2 toss_winner toss_decision winner result result_margin eliminator method umpire1 umpire2
0 335982 Bangalore 2008-04-18 BB McCullum M Chinnaswamy Stadium 0 Royal Challengers Bangalore Kolkata Knight Riders Royal Challengers Bangalore field Kolkata Knight Riders runs 140.0 N NaN Asad Rauf RE Koertzen
1 335983 Chandigarh 2008-04-19 MEK Hussey Punjab Cricket Association Stadium, Mohali 0 Kings XI Punjab Chennai Super Kings Chennai Super Kings bat Chennai Super Kings runs 33.0 N NaN MR Benson SL Shastri
2 335984 Delhi 2008-04-19 MF Maharoof Feroz Shah Kotla 0 Delhi Daredevils Rajasthan Royals Rajasthan Royals bat Delhi Daredevils wickets 9.0 N NaN Aleem Dar GA Pratapkumar
3 335985 Mumbai 2008-04-20 MV Boucher Wankhede Stadium 0 Mumbai Indians Royal Challengers Bangalore Mumbai Indians bat Royal Challengers Bangalore wickets 5.0 N NaN SJ Davis DJ Harper
4 335986 Kolkata 2008-04-20 DJ Hussey Eden Gardens 0 Kolkata Knight Riders Deccan Chargers Deccan Chargers bat Kolkata Knight Riders wickets 5.0 N NaN BF Bowden K Hariharan
In [6]:
df2.shape  # number of rows and columns
Out[6]:
(816, 17)
In [7]:
df2.shape[0]  # number of rows
Out[7]:
816
In [8]:
df2.shape[1] # number of columns
Out[8]:
17
In [9]:
# Name of all the columns(labels)
df2.columns
Out[9]:
Index(['id', 'city', 'date', 'player_of_match', 'venue', 'neutral_venue',
       'team1', 'team2', 'toss_winner', 'toss_decision', 'winner', 'result',
       'result_margin', 'eliminator', 'method', 'umpire1', 'umpire2'],
      dtype='object')
In [10]:
df2['city'].unique()
Out[10]:
array(['Bangalore', 'Chandigarh', 'Delhi', 'Mumbai', 'Kolkata', 'Jaipur',
       'Hyderabad', 'Chennai', 'Cape Town', 'Port Elizabeth', 'Durban',
       'Centurion', 'East London', 'Johannesburg', 'Kimberley',
       'Bloemfontein', 'Ahmedabad', 'Cuttack', 'Nagpur', 'Dharamsala',
       'Kochi', 'Indore', 'Visakhapatnam', 'Pune', 'Raipur', 'Ranchi',
       'Abu Dhabi', nan, 'Rajkot', 'Kanpur', 'Bengaluru', 'Dubai',
       'Sharjah'], dtype=object)
In [11]:
df2['team1'].unique()
Out[11]:
array(['Royal Challengers Bangalore', 'Kings XI Punjab',
       'Delhi Daredevils', 'Mumbai Indians', 'Kolkata Knight Riders',
       'Rajasthan Royals', 'Deccan Chargers', 'Chennai Super Kings',
       'Kochi Tuskers Kerala', 'Pune Warriors', 'Sunrisers Hyderabad',
       'Gujarat Lions', 'Rising Pune Supergiants',
       'Rising Pune Supergiant', 'Delhi Capitals'], dtype=object)
In [12]:
df2['team2'].unique()
Out[12]:
array(['Kolkata Knight Riders', 'Chennai Super Kings', 'Rajasthan Royals',
       'Royal Challengers Bangalore', 'Deccan Chargers',
       'Kings XI Punjab', 'Delhi Daredevils', 'Mumbai Indians',
       'Kochi Tuskers Kerala', 'Pune Warriors', 'Sunrisers Hyderabad',
       'Rising Pune Supergiants', 'Gujarat Lions',
       'Rising Pune Supergiant', 'Delhi Capitals'], dtype=object)
In [13]:
df2['venue'].value_counts()
Out[13]:
Eden Gardens                                            77
Feroz Shah Kotla                                        74
Wankhede Stadium                                        73
M Chinnaswamy Stadium                                   65
Rajiv Gandhi International Stadium, Uppal               64
MA Chidambaram Stadium, Chepauk                         57
Sawai Mansingh Stadium                                  47
Punjab Cricket Association Stadium, Mohali              35
Dubai International Cricket Stadium                     33
Sheikh Zayed Stadium                                    29
Punjab Cricket Association IS Bindra Stadium, Mohali    21
Maharashtra Cricket Association Stadium                 21
Sharjah Cricket Stadium                                 18
Dr DY Patil Sports Academy                              17
Subrata Roy Sahara Stadium                              17
M.Chinnaswamy Stadium                                   15
Kingsmead                                               15
Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium     13
Sardar Patel Stadium, Motera                            12
SuperSport Park                                         12
Brabourne Stadium                                       11
Saurashtra Cricket Association Stadium                  10
Himachal Pradesh Cricket Association Stadium             9
Holkar Cricket Stadium                                   9
New Wanderers Stadium                                    8
Barabati Stadium                                         7
Newlands                                                 7
St George's Park                                         7
JSCA International Stadium Complex                       7
Shaheed Veer Narayan Singh International Stadium         6
Nehru Stadium                                            5
Green Park                                               4
Buffalo Park                                             3
Vidarbha Cricket Association Stadium, Jamtha             3
De Beers Diamond Oval                                    3
OUTsurance Oval                                          2
Name: venue, dtype: int64

Let's remove useless columns¶

In [14]:
df2_new=df2.drop(labels=['date','neutral_venue','method'],axis=1)
In [15]:
df2_new.head()
Out[15]:
id city player_of_match venue team1 team2 toss_winner toss_decision winner result result_margin eliminator umpire1 umpire2
0 335982 Bangalore BB McCullum M Chinnaswamy Stadium Royal Challengers Bangalore Kolkata Knight Riders Royal Challengers Bangalore field Kolkata Knight Riders runs 140.0 N Asad Rauf RE Koertzen
1 335983 Chandigarh MEK Hussey Punjab Cricket Association Stadium, Mohali Kings XI Punjab Chennai Super Kings Chennai Super Kings bat Chennai Super Kings runs 33.0 N MR Benson SL Shastri
2 335984 Delhi MF Maharoof Feroz Shah Kotla Delhi Daredevils Rajasthan Royals Rajasthan Royals bat Delhi Daredevils wickets 9.0 N Aleem Dar GA Pratapkumar
3 335985 Mumbai MV Boucher Wankhede Stadium Mumbai Indians Royal Challengers Bangalore Mumbai Indians bat Royal Challengers Bangalore wickets 5.0 N SJ Davis DJ Harper
4 335986 Kolkata DJ Hussey Eden Gardens Kolkata Knight Riders Deccan Chargers Deccan Chargers bat Kolkata Knight Riders wickets 5.0 N BF Bowden K Hariharan
In [16]:
df2_new.shape
Out[16]:
(816, 14)
In [17]:
sns.heatmap(df2.isnull(),cbar=False,cmap='viridis')
plt.show()

Observation:¶

We observe that yellow color show the null values.

Find Missing Values¶

In [18]:
df2_new.isnull().sum()
Out[18]:
id                  0
city               13
player_of_match     4
venue               0
team1               0
team2               0
toss_winner         0
toss_decision       0
winner              4
result              4
result_margin      17
eliminator          4
umpire1             0
umpire2             0
dtype: int64

Tackling with missing values of "city"¶

In [ ]:

observations:¶

We observe that rows consisting of 'Sharjah Cricket Stadium' and 'Dubai International Cricket Stadium' only have their "city" values missing. Hence we would be filling the null values with Sharjah and Dubai respectively.

In [19]:
for i in df2_new[df2_new['city'].isna()].index:
        df2_new.loc[i, 'city'] = 'Sharjah' if df2_new.loc[i, 'venue'] == 'Sharjah Cricket Stadium' else 'Dubai'
In [20]:
df2_new['city'].unique()
Out[20]:
array(['Bangalore', 'Chandigarh', 'Delhi', 'Mumbai', 'Kolkata', 'Jaipur',
       'Hyderabad', 'Chennai', 'Cape Town', 'Port Elizabeth', 'Durban',
       'Centurion', 'East London', 'Johannesburg', 'Kimberley',
       'Bloemfontein', 'Ahmedabad', 'Cuttack', 'Nagpur', 'Dharamsala',
       'Kochi', 'Indore', 'Visakhapatnam', 'Pune', 'Raipur', 'Ranchi',
       'Abu Dhabi', 'Sharjah', 'Dubai', 'Rajkot', 'Kanpur', 'Bengaluru'],
      dtype=object)
In [21]:
df2_new.isnull().sum()
Out[21]:
id                  0
city                0
player_of_match     4
venue               0
team1               0
team2               0
toss_winner         0
toss_decision       0
winner              4
result              4
result_margin      17
eliminator          4
umpire1             0
umpire2             0
dtype: int64
In [22]:
df2_new[df2_new['player_of_match'].isnull()]
Out[22]:
id city player_of_match venue team1 team2 toss_winner toss_decision winner result result_margin eliminator umpire1 umpire2
241 501265 Delhi NaN Feroz Shah Kotla Delhi Daredevils Pune Warriors Delhi Daredevils bat NaN NaN NaN NaN SS Hazare RJ Tucker
486 829763 Bangalore NaN M Chinnaswamy Stadium Royal Challengers Bangalore Rajasthan Royals Rajasthan Royals field NaN NaN NaN NaN JD Cloete PG Pathak
511 829813 Bangalore NaN M Chinnaswamy Stadium Royal Challengers Bangalore Delhi Daredevils Royal Challengers Bangalore field NaN NaN NaN NaN HDPK Dharmasena K Srinivasan
744 1178424 Bengaluru NaN M.Chinnaswamy Stadium Royal Challengers Bangalore Rajasthan Royals Rajasthan Royals field NaN NaN NaN NaN UV Gandhe NJ Llong

Observation¶

We Observe that we don't have any values of the "player_of_match", "winner", "result" , "result_margin" ,"eliminator".

All these values remain missing due to rain.

Hence we will fill these NaN values with "no result"

In [23]:
def result(): #passing the dataframe as the parameter
    for i in df2_new.loc[df2_new['result'].isna()].index:
        
        df2_new.loc[i,'winner']="No result"
        df2_new.loc[i,'result']="No result"
        df2_new.loc[i,'result_margin']="No result"
       
       
result()
In [24]:
df2_new['player_of_match']= df2_new['player_of_match'].fillna("No result")
df2_new['eliminator']= df2_new['eliminator'].fillna("No result")
In [26]:
df2_new[df2_new['result_margin'].isnull()]
Out[26]:
id city player_of_match venue team1 team2 toss_winner toss_decision winner result result_margin eliminator umpire1 umpire2
66 392190 Cape Town YK Pathan Newlands Kolkata Knight Riders Rajasthan Royals Kolkata Knight Riders field Rajasthan Royals tie NaN Y MR Benson M Erasmus
130 419121 Chennai J Theron MA Chidambaram Stadium, Chepauk Chennai Super Kings Kings XI Punjab Chennai Super Kings field Kings XI Punjab tie NaN Y K Hariharan DJ Harper
328 598004 Hyderabad GH Vihari Rajiv Gandhi International Stadium, Uppal Sunrisers Hyderabad Royal Challengers Bangalore Royal Challengers Bangalore bat Sunrisers Hyderabad tie NaN Y AK Chaudhary S Ravi
341 598017 Bangalore V Kohli M Chinnaswamy Stadium Royal Challengers Bangalore Delhi Daredevils Royal Challengers Bangalore field Royal Challengers Bangalore tie NaN Y M Erasmus VA Kulkarni
416 729315 Abu Dhabi JP Faulkner Sheikh Zayed Stadium Kolkata Knight Riders Rajasthan Royals Rajasthan Royals bat Rajasthan Royals tie NaN Y Aleem Dar AK Chaudhary
476 829741 Ahmedabad SE Marsh Sardar Patel Stadium, Motera Rajasthan Royals Kings XI Punjab Kings XI Punjab field Kings XI Punjab tie NaN Y M Erasmus S Ravi
610 1082625 Rajkot KH Pandya Saurashtra Cricket Association Stadium Gujarat Lions Mumbai Indians Gujarat Lions bat Mumbai Indians tie NaN Y AK Chaudhary CB Gaffaney
705 1175365 Delhi PP Shaw Feroz Shah Kotla Delhi Capitals Kolkata Knight Riders Delhi Capitals field Delhi Capitals tie NaN Y AY Dandekar Nitin Menon
746 1178426 Mumbai JJ Bumrah Wankhede Stadium Mumbai Indians Sunrisers Hyderabad Mumbai Indians bat Mumbai Indians tie NaN Y CK Nandan S Ravi
757 1216493 Dubai MP Stoinis Dubai International Cricket Stadium Delhi Capitals Kings XI Punjab Kings XI Punjab field Delhi Capitals tie NaN Y AK Chaudhary Nitin Menon
776 1216512 Abu Dhabi LH Ferguson Sheikh Zayed Stadium Kolkata Knight Riders Sunrisers Hyderabad Sunrisers Hyderabad field Kolkata Knight Riders tie NaN Y PG Pathak S Ravi
781 1216517 Dubai KL Rahul Dubai International Cricket Stadium Mumbai Indians Kings XI Punjab Mumbai Indians bat Kings XI Punjab tie NaN Y Nitin Menon PR Reiffel
811 1216547 Dubai AB de Villiers Dubai International Cricket Stadium Royal Challengers Bangalore Mumbai Indians Mumbai Indians field Royal Challengers Bangalore tie NaN Y Nitin Menon PR Reiffel

Observations:¶

We observe that we got NaN values in 'result_margin' because result was tied.

So we fill these NaN values with "no result".

In [27]:
df2_new['result_margin']= df2_new['result_margin'].fillna(0)
In [28]:
df2_new.isnull().sum()
Out[28]:
id                 0
city               0
player_of_match    0
venue              0
team1              0
team2              0
toss_winner        0
toss_decision      0
winner             0
result             0
result_margin      0
eliminator         0
umpire1            0
umpire2            0
dtype: int64

No missing values in the entire dataset .¶

In [29]:
runs=df2_new[df2_new['result']=='runs']
wickets=df2_new[df2_new['result']=='wickets']
In [30]:
sns.countplot(df2_new.toss_decision)

C:\Users\HP\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
Out[30]:
<AxesSubplot:xlabel='toss_decision', ylabel='count'>
In [31]:
plt.figure(figsize=(14,8))
sns.countplot(df2_new.result)

C:\Users\HP\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
Out[31]:
<AxesSubplot:xlabel='result', ylabel='count'>
In [32]:
df2_new["winner"].value_counts()
Out[32]:
Mumbai Indians                 120
Chennai Super Kings            106
Kolkata Knight Riders           99
Royal Challengers Bangalore     91
Kings XI Punjab                 88
Rajasthan Royals                81
Delhi Daredevils                67
Sunrisers Hyderabad             66
Deccan Chargers                 29
Delhi Capitals                  19
Gujarat Lions                   13
Pune Warriors                   12
Rising Pune Supergiant          10
Kochi Tuskers Kerala             6
Rising Pune Supergiants          5
No result                        4
Name: winner, dtype: int64
In [33]:
df2_new["toss_winner"].value_counts()
Out[33]:
Mumbai Indians                 106
Kolkata Knight Riders           98
Chennai Super Kings             97
Royal Challengers Bangalore     87
Rajasthan Royals                87
Kings XI Punjab                 85
Delhi Daredevils                80
Sunrisers Hyderabad             57
Deccan Chargers                 43
Pune Warriors                   20
Delhi Capitals                  20
Gujarat Lions                   15
Kochi Tuskers Kerala             8
Rising Pune Supergiants          7
Rising Pune Supergiant           6
Name: toss_winner, dtype: int64
In [34]:
plt.figure(figsize=(20,5))
x=df2_new['toss_winner'].value_counts().keys()
y=df2_new['toss_winner'].value_counts()
team=sns.barplot(x,y)
plt.xlabel('Team',fontsize=15)
plt.ylabel('Count',fontsize=15)
plt.title('Number of times a team won the toss',pad=15,fontsize=15)
team.set_xticklabels(rotation=90,labels=x,fontsize=15)

C:\Users\HP\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
Out[34]:
[Text(0, 0, 'Mumbai Indians'),
 Text(1, 0, 'Kolkata Knight Riders'),
 Text(2, 0, 'Chennai Super Kings'),
 Text(3, 0, 'Royal Challengers Bangalore'),
 Text(4, 0, 'Rajasthan Royals'),
 Text(5, 0, 'Kings XI Punjab'),
 Text(6, 0, 'Delhi Daredevils'),
 Text(7, 0, 'Sunrisers Hyderabad'),
 Text(8, 0, 'Deccan Chargers'),
 Text(9, 0, 'Pune Warriors'),
 Text(10, 0, 'Delhi Capitals'),
 Text(11, 0, 'Gujarat Lions'),
 Text(12, 0, 'Kochi Tuskers Kerala'),
 Text(13, 0, 'Rising Pune Supergiants'),
 Text(14, 0, 'Rising Pune Supergiant')]

Observation:¶

Mumbai indians won the most number of tosses followed by the Kolkata knight Riders and chennai Super kings.

In [35]:
plt.figure(figsize=(12,12))
plt.pie(list(df2_new['winner'].value_counts()),labels=list(df2_new['winner'].value_counts().keys()),autopct='%0.1f%%')
plt.show()
In [36]:
plt.figure(figsize=(20,5))
x=df2_new['winner'].value_counts().keys()
y=df2_new['winner'].value_counts()
team=sns.barplot(x,y)
plt.xlabel('Team',fontsize=15)
plt.ylabel('Count',fontsize=15)
plt.title('Number of times a team won the match',pad=15,fontsize=15)
team.set_xticklabels(rotation=90,labels=x,fontsize=15)

C:\Users\HP\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
Out[36]:
[Text(0, 0, 'Mumbai Indians'),
 Text(1, 0, 'Chennai Super Kings'),
 Text(2, 0, 'Kolkata Knight Riders'),
 Text(3, 0, 'Royal Challengers Bangalore'),
 Text(4, 0, 'Kings XI Punjab'),
 Text(5, 0, 'Rajasthan Royals'),
 Text(6, 0, 'Delhi Daredevils'),
 Text(7, 0, 'Sunrisers Hyderabad'),
 Text(8, 0, 'Deccan Chargers'),
 Text(9, 0, 'Delhi Capitals'),
 Text(10, 0, 'Gujarat Lions'),
 Text(11, 0, 'Pune Warriors'),
 Text(12, 0, 'Rising Pune Supergiant'),
 Text(13, 0, 'Kochi Tuskers Kerala'),
 Text(14, 0, 'Rising Pune Supergiants'),
 Text(15, 0, 'No result')]

Observations:¶

1.Mumbai indians won the most number of matches followed by the chennai Super kings and Kolkata knight Riders.

In [37]:
plt.figure(figsize=(20,5))
x=df2['player_of_match'].value_counts()[0:10]
y=df2['player_of_match'].value_counts().keys()[0:10]
sns.barplot(y,x)
plt.title('Top 10 Players With Most Player of Match',fontsize=15)
plt.xlabel('Player of Match',fontsize=15)

C:\Users\HP\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
Out[37]:
Text(0.5, 0, 'Player of Match')
In [38]:
df2_new.boxplot()
Out[38]:
<AxesSubplot:>
In [39]:
df2_new.hist()
Out[39]:
array([[<AxesSubplot:title={'center':'id'}>]], dtype=object)
In [36]:
sns.pairplot(df2_new)
Out[36]:
<seaborn.axisgrid.PairGrid at 0x1af94811760>
In [37]:
sns.heatmap(df2_new.isnull(),cbar=False,cmap='viridis')
Out[37]:
<AxesSubplot:>

Observation:¶

We Observe that there is no null values.

In [38]:
um = pd.concat([df2_new['umpire1'],df2_new['umpire2']])
um = um.value_counts()
um = um.to_frame().reset_index()
um.head(12)
Out[38]:
index 0
0 S Ravi 121
1 HDPK Dharmasena 94
2 AK Chaudhary 87
3 C Shamshuddin 82
4 M Erasmus 65
5 CK Nandan 57
6 Nitin Menon 57
7 SJA Taufel 55
8 Asad Rauf 51
9 VA Kulkarni 50
10 BNJ Oxenford 48
11 CB Gaffaney 47
In [39]:
fig = px.box(df2_new, y="result_margin", x="city")
fig.show()
In [41]:
fig,ax=plt.subplots(1,2,figsize=(10,5),sharey=True)
sns.histplot(runs['result_margin'],ax=ax[0])
ax[0].set_title('Win by Runs',pad=15)
ax[0].set_xlabel('Margin')

sns.histplot(wickets['result_margin'],ax=ax[1])
ax[1].set_title('Win by Wickets',pad=15)
ax[1].set_xlabel('Margin')

sns.despine(fig=None)

Batsman¶

In [42]:
df1
Out[42]:
id inning over ball batsman non_striker bowler batsman_runs extra_runs total_runs non_boundary is_wicket dismissal_kind player_dismissed fielder extras_type batting_team bowling_team
0 335982 1 6 5 RT Ponting BB McCullum AA Noffke 1 0 1 0 0 NaN NaN NaN NaN Kolkata Knight Riders Royal Challengers Bangalore
1 335982 1 6 6 BB McCullum RT Ponting AA Noffke 1 0 1 0 0 NaN NaN NaN NaN Kolkata Knight Riders Royal Challengers Bangalore
2 335982 1 7 1 BB McCullum RT Ponting Z Khan 0 0 0 0 0 NaN NaN NaN NaN Kolkata Knight Riders Royal Challengers Bangalore
3 335982 1 7 2 BB McCullum RT Ponting Z Khan 1 0 1 0 0 NaN NaN NaN NaN Kolkata Knight Riders Royal Challengers Bangalore
4 335982 1 7 3 RT Ponting BB McCullum Z Khan 1 0 1 0 0 NaN NaN NaN NaN Kolkata Knight Riders Royal Challengers Bangalore
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
193463 1237181 1 12 5 RR Pant SS Iyer NM Coulter-Nile 0 0 0 0 0 NaN NaN NaN NaN Delhi Capitals Mumbai Indians
193464 1237181 1 12 6 RR Pant SS Iyer NM Coulter-Nile 1 0 1 0 0 NaN NaN NaN NaN Delhi Capitals Mumbai Indians
193465 1237181 1 13 1 RR Pant SS Iyer KH Pandya 0 1 1 0 0 NaN NaN NaN wides Delhi Capitals Mumbai Indians
193466 1237181 1 13 2 RR Pant SS Iyer KH Pandya 1 0 1 0 0 NaN NaN NaN NaN Delhi Capitals Mumbai Indians
193467 1237181 1 13 3 SS Iyer RR Pant KH Pandya 1 0 1 0 0 NaN NaN NaN NaN Delhi Capitals Mumbai Indians

193468 rows × 18 columns

Basic information about the dataset¶

In [43]:
df1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 193468 entries, 0 to 193467
Data columns (total 18 columns):
 #   Column            Non-Null Count   Dtype 
---  ------            --------------   ----- 
 0   id                193468 non-null  int64 
 1   inning            193468 non-null  int64 
 2   over              193468 non-null  int64 
 3   ball              193468 non-null  int64 
 4   batsman           193468 non-null  object
 5   non_striker       193468 non-null  object
 6   bowler            193468 non-null  object
 7   batsman_runs      193468 non-null  int64 
 8   extra_runs        193468 non-null  int64 
 9   total_runs        193468 non-null  int64 
 10  non_boundary      193468 non-null  int64 
 11  is_wicket         193468 non-null  int64 
 12  dismissal_kind    9495 non-null    object
 13  player_dismissed  9495 non-null    object
 14  fielder           6784 non-null    object
 15  extras_type       10233 non-null   object
 16  batting_team      193468 non-null  object
 17  bowling_team      193277 non-null  object
dtypes: int64(9), object(9)
memory usage: 26.6+ MB
In [44]:
df1.shape
Out[44]:
(193468, 18)
In [45]:
df1.columns
Out[45]:
Index(['id', 'inning', 'over', 'ball', 'batsman', 'non_striker', 'bowler',
       'batsman_runs', 'extra_runs', 'total_runs', 'non_boundary', 'is_wicket',
       'dismissal_kind', 'player_dismissed', 'fielder', 'extras_type',
       'batting_team', 'bowling_team'],
      dtype='object')
In [46]:
df1.describe()
Out[46]:
id inning over ball batsman_runs extra_runs total_runs non_boundary is_wicket
count 1.934680e+05 193468.000000 193468.000000 193468.000000 193468.000000 193468.000000 193468.000000 193468.000000 193468.000000
mean 7.567688e+05 1.482131 9.177027 3.615967 1.240231 0.066414 1.306645 0.000083 0.049078
std 3.060971e+05 0.499682 5.676848 1.807128 1.610867 0.339991 1.598802 0.009094 0.216031
min 3.359820e+05 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 5.012270e+05 1.000000 4.000000 2.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 7.292970e+05 1.000000 9.000000 4.000000 1.000000 0.000000 1.000000 0.000000 0.000000
75% 1.082628e+06 2.000000 14.000000 5.000000 1.000000 0.000000 1.000000 0.000000 0.000000
max 1.237181e+06 2.000000 19.000000 9.000000 6.000000 7.000000 7.000000 1.000000 1.000000

Find missing values¶

In [47]:
df1.isnull().sum()
Out[47]:
id                       0
inning                   0
over                     0
ball                     0
batsman                  0
non_striker              0
bowler                   0
batsman_runs             0
extra_runs               0
total_runs               0
non_boundary             0
is_wicket                0
dismissal_kind      183973
player_dismissed    183973
fielder             186684
extras_type         183235
batting_team             0
bowling_team           191
dtype: int64
In [48]:
sns.heatmap(df1.isnull(),cbar=False,cmap='viridis')
plt.show()

Observation:¶

We observe that yellow color show the null values.

Let's remove useless columns¶

In [49]:
df1_new=df1.drop(labels=['dismissal_kind','player_dismissed','fielder','extras_type'],axis=1)
In [50]:
df1_new.head()
Out[50]:
id inning over ball batsman non_striker bowler batsman_runs extra_runs total_runs non_boundary is_wicket batting_team bowling_team
0 335982 1 6 5 RT Ponting BB McCullum AA Noffke 1 0 1 0 0 Kolkata Knight Riders Royal Challengers Bangalore
1 335982 1 6 6 BB McCullum RT Ponting AA Noffke 1 0 1 0 0 Kolkata Knight Riders Royal Challengers Bangalore
2 335982 1 7 1 BB McCullum RT Ponting Z Khan 0 0 0 0 0 Kolkata Knight Riders Royal Challengers Bangalore
3 335982 1 7 2 BB McCullum RT Ponting Z Khan 1 0 1 0 0 Kolkata Knight Riders Royal Challengers Bangalore
4 335982 1 7 3 RT Ponting BB McCullum Z Khan 1 0 1 0 0 Kolkata Knight Riders Royal Challengers Bangalore
In [51]:
df1_new.shape
Out[51]:
(193468, 14)
In [52]:
df1_new.isnull().sum()
Out[52]:
id                0
inning            0
over              0
ball              0
batsman           0
non_striker       0
bowler            0
batsman_runs      0
extra_runs        0
total_runs        0
non_boundary      0
is_wicket         0
batting_team      0
bowling_team    191
dtype: int64
In [53]:
df1_new[df1_new['bowling_team'].isnull()]
Out[53]:
id inning over ball batsman non_striker bowler batsman_runs extra_runs total_runs non_boundary is_wicket batting_team bowling_team
57048 501265 1 0 1 NV Ojha DA Warner AC Thomas 1 0 1 0 0 Delhi Daredevils NaN
57049 501265 1 0 2 DA Warner NV Ojha AC Thomas 0 1 1 0 0 Delhi Daredevils NaN
57050 501265 1 1 1 DA Warner NV Ojha JP Faulkner 0 1 1 0 0 Delhi Daredevils NaN
57051 501265 1 1 2 NV Ojha DA Warner JP Faulkner 0 0 0 0 0 Delhi Daredevils NaN
57052 501265 1 1 3 NV Ojha DA Warner JP Faulkner 0 0 0 0 0 Delhi Daredevils NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
115654 829763 1 7 3 AB de Villiers Mandeep Singh STR Binny 0 0 0 0 0 Royal Challengers Bangalore NaN
115655 829763 1 8 6 AB de Villiers Mandeep Singh SR Watson 2 0 2 0 0 Royal Challengers Bangalore NaN
115656 829763 1 9 1 Mandeep Singh AB de Villiers STR Binny 4 0 4 0 0 Royal Challengers Bangalore NaN
115657 829763 1 8 4 AB de Villiers Mandeep Singh SR Watson 0 0 0 0 0 Royal Challengers Bangalore NaN
115658 829763 1 8 5 AB de Villiers Mandeep Singh SR Watson 0 0 0 0 0 Royal Challengers Bangalore NaN

191 rows × 14 columns

In [54]:
df1_new['bowling_team'].unique()
Out[54]:
array(['Royal Challengers Bangalore', 'Kolkata Knight Riders',
       'Chennai Super Kings', 'Kings XI Punjab', 'Delhi Daredevils',
       'Rajasthan Royals', 'Mumbai Indians', 'Deccan Chargers',
       'Kochi Tuskers Kerala', 'Pune Warriors', nan,
       'Sunrisers Hyderabad', 'Rising Pune Supergiants', 'Gujarat Lions',
       'Rising Pune Supergiant', 'Delhi Capitals'], dtype=object)
In [55]:
correlation = df1_new.corr()
In [56]:
plt.figure(figsize=(18, 18))



sns.heatmap(correlation, xticklabels=correlation.columns, yticklabels=correlation.columns, annot=True)
Out[56]:
<AxesSubplot:>
In [57]:
df1_new['bowling_team'].value_counts()
Out[57]:
Mumbai Indians                 24453
Royal Challengers Bangalore    23024
Kolkata Knight Riders          22583
Kings XI Punjab                22457
Chennai Super Kings            21224
Rajasthan Royals               18972
Delhi Daredevils               18719
Sunrisers Hyderabad            14703
Deccan Chargers                 9039
Pune Warriors                   5394
Delhi Capitals                  4012
Gujarat Lions                   3540
Rising Pune Supergiant          1928
Rising Pune Supergiants         1615
Kochi Tuskers Kerala            1614
Name: bowling_team, dtype: int64
In [58]:
df1_new['bowling_team'].fillna(df1_new["bowling_team"].mode()[0],inplace=True)
In [ ]:
In [59]:
df1_new.isnull().sum()
Out[59]:
id              0
inning          0
over            0
ball            0
batsman         0
non_striker     0
bowler          0
batsman_runs    0
extra_runs      0
total_runs      0
non_boundary    0
is_wicket       0
batting_team    0
bowling_team    0
dtype: int64

Observation:¶

We Observe that there is no null values.

In [60]:
df1_new.describe()
Out[60]:
id inning over ball batsman_runs extra_runs total_runs non_boundary is_wicket
count 1.934680e+05 193468.000000 193468.000000 193468.000000 193468.000000 193468.000000 193468.000000 193468.000000 193468.000000
mean 7.567688e+05 1.482131 9.177027 3.615967 1.240231 0.066414 1.306645 0.000083 0.049078
std 3.060971e+05 0.499682 5.676848 1.807128 1.610867 0.339991 1.598802 0.009094 0.216031
min 3.359820e+05 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 5.012270e+05 1.000000 4.000000 2.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 7.292970e+05 1.000000 9.000000 4.000000 1.000000 0.000000 1.000000 0.000000 0.000000
75% 1.082628e+06 2.000000 14.000000 5.000000 1.000000 0.000000 1.000000 0.000000 0.000000
max 1.237181e+06 2.000000 19.000000 9.000000 6.000000 7.000000 7.000000 1.000000 1.000000
In [61]:
sns.pairplot(df1_new,height=1.5)
Out[61]:
<seaborn.axisgrid.PairGrid at 0x1afa23d35b0>
In [62]:
sns.pairplot(df1_new , height=1.5)
Out[62]:
<seaborn.axisgrid.PairGrid at 0x1afa26c5e50>
In [63]:
plt.figure(figsize=(20,5))
x=df1_new['batsman'].value_counts()[0:20]
y=df1_new['batsman'].value_counts().keys()[0:20]
sns.barplot(y,x)
plt.title('Top 20 batsman With Most Player of Match',fontsize=15)
plt.xlabel('Player of Match',fontsize=15)

C:\Users\HP\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
Out[63]:
Text(0.5, 0, 'Player of Match')
In [86]:
plt.figure(figsize=(20,5))
x=df1_new['bowling_team'].value_counts().keys()[0:20]
y=df1_new['bowling_team'].value_counts()[0:20]
team=sns.barplot(x,y)
plt.xlabel('Team',fontsize=15)
plt.ylabel('Count',fontsize=15)

team.set_xticklabels(rotation=90,labels=x,fontsize=15)

C:\Users\HP\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
Out[86]:
[Text(0, 0, 'Mumbai Indians'),
 Text(1, 0, 'Royal Challengers Bangalore'),
 Text(2, 0, 'Kolkata Knight Riders'),
 Text(3, 0, 'Kings XI Punjab'),
 Text(4, 0, 'Chennai Super Kings'),
 Text(5, 0, 'Rajasthan Royals'),
 Text(6, 0, 'Delhi Daredevils'),
 Text(7, 0, 'Sunrisers Hyderabad'),
 Text(8, 0, 'Deccan Chargers'),
 Text(9, 0, 'Pune Warriors'),
 Text(10, 0, 'Delhi Capitals'),
 Text(11, 0, 'Gujarat Lions'),
 Text(12, 0, 'Rising Pune Supergiant'),
 Text(13, 0, 'Rising Pune Supergiants'),
 Text(14, 0, 'Kochi Tuskers Kerala')]
In [ ]:
In [ ]: